In this section
@article{Wang:2024:10.1145/3698105,
author = {Wang, Q and Dai, H-N and Yang, J and Guo, C and Childs, P and Kleinsmann, M and Guo, Y and Wang, P},
doi = {10.1145/3698105},
journal = {ACM Computing Surveys},
title = {Learning-based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation},
url = {http://dx.doi.org/10.1145/3698105},
year = {2024}
}
TY - JOUR
AB - <jats:p>With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering (NPR) and, latterly, neural-style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalised methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation and development of AI artwork methods face many challenges. This paper is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.</jats:p>
AU - Wang,Q
AU - Dai,H-N
AU - Yang,J
AU - Guo,C
AU - Childs,P
AU - Kleinsmann,M
AU - Guo,Y
AU - Wang,P
DO - 10.1145/3698105
PY - 2024///
SN - 0360-0300
TI - Learning-based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation
T2 - ACM Computing Surveys
UR - http://dx.doi.org/10.1145/3698105
ER -
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